Detection and visualization of geographic data

ABSTRACT

Identifying and visualizing geographic data includes obtaining a set of data including candidate geographic data elements. Metrics are determined based on two or more of: a best parent for the candidate geographic data elements; additional concepts associated with the candidate geographic data elements; and an average distance between the candidate geographic data elements. Then, the candidate geographic data elements are identified as geographic based on the metrics and a map is generated displaying the candidate geographic data elements identified as geographic.

BACKGROUND

Present invention embodiments relate to data processing systems, andmore specifically, to techniques for identifying and visualizinggeographic data with data processing systems.

Geographic data is widely utilized and can be incredibly valuable. Forexample, with the rise of cloud computing systems, mobile connectivity,and other technology that allows users to remain connected while atdifferent locations or on the move, geographic data may allow differentsystems to provide services based on the geographic location of a user.However, it is surprisingly difficult to identify a set of data asgeographic, especially when a dataset is a string of text, withoutmetadata, unambiguous labels, or other such data that might signify whattype of data is included in the data set. For example, in IBM® WATSONANALYTICS™, users can upload large or small amounts of data in order tovisualize their data; however, the data may be uploaded as a commaseparated values (CSV) file without any metadata identifying the dataincluded therein and, thus it may be difficult to identify geographicdata included therein. This is exacerbated when the geographic data isgeographic data from a lower level in a geographic hierarchy, such ascities, towns, or even counties, as opposed to countries or states. Forexample, counties in the United States may be named Jefferson, Davis,Montgomery, and other such names that may also be common family names(e.g., last names) in the United States.

SUMMARY

According to one embodiment of the present invention, identifying andvisualizing geographic data includes obtaining a set of data includingcandidate geographic data elements. Metrics are determined based on twoor more of: a best parent for the candidate geographic data elements;additional concepts associated with the candidate geographic dataelements; and an average distance between the candidate geographic dataelements. Then, the candidate geographic data elements are identified asgeographic based on the metrics and a map is generated displaying thecandidate geographic data elements identified as geographic.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilizedto designate like components.

FIG. 1 illustrates an example environment in which the present generalinventive concept can be embodied.

FIG. 2 is a procedural flow chart of identifying and visualizinggeographic data, according to a present invention embodiment.

FIG. 3 is a procedural flow chart illustrating operations for utilizinga best parent to identify geographic data, according to a presentinvention embodiment.

FIG. 4 is a procedural flow chart illustrating operations fordetermining a likelihood score to identify geographic data, according toa present invention embodiment.

FIG. 5 is a block diagram of a computing node according to an embodimentof the present invention.

DETAILED DESCRIPTION

Presented herein are techniques for identifying or detecting geographicdata and visualizing the identified geographic data. Generally, thetechniques provided herein utilize a unique combination of tests inorder to identify data from a data set, such as a structured data set,as geographic data with a high degree of accuracy. Then, the identifieddata can be displayed on a map in order to visualize the data. Thetechniques determine the likelihood that candidate data elementsincluded in the data set represent geographic elements by focusing on abest parent of the candidate elements, distances between the candidateelements, and the number of other concepts that may be associated withthe candidate elements. As is described in more detail below, the bestparent may be a geographic element that covers a substantial subset ofthe candidate elements, thereby revealing similarities in the candidatedata elements. When the best parent is used in combination with theaverage distance and number of other concepts, geographic data may beidentified with a high degree of accuracy, thereby allowing applicationsor processes utilizing geographic data to function more efficiently. Forexample, accurately identifying geographic data allows data to beproperly visualized (e.g., on a map).

Without these techniques, approaches may be required to rely on labelsor tags included in a data set, which is often ineffective since datamay be labeled with customized or internally understood labels, asopposed to standardized labels. For example, a user could enter anacronym column name, such as columnName=“MUCN,” where MUCN stood for “MyUser's County Name,” and this column name may be unidentifiable withoutan explanation of the acronym. Moreover, many data sets are oftenunlabeled. To address this, other alternative approaches may analyzemetadata or a dictionary; however, this may also experience issues withunidentifiable labels or unlabeled data, may be inefficient in terms ofboth time and resources, and/or may be inaccurate.

An example environment for use with present invention embodiments isillustrated in FIG. 1. Specifically, the environment includes one ormore server systems 10, and one or more client or end-user systems 14.The server systems 10 and client systems 14 are each described infurther detail below in connection with FIG. 6; however, generally,server systems 10 and client systems 14 may be remote from each otherand communicate over a network 12. The network 12 may be implemented byany number of any suitable communications media (e.g., wide area network(WAN), local area network (LAN), Internet, Intranet, etc.).Alternatively, server systems 10 and client systems 14 may be local toeach other, and communicate via any appropriate local communicationmedium (e.g., local area network (LAN), hardwire, wireless link,Intranet, etc.).

In the present invention embodiment depicted in FIG. 1, the serversystem 10 includes a geographic data module 16; however, as shown indashed lines, in other embodiments, the geographic data module 16 mayalso be disposed, either entirely or partially, on the client systems14. Geographic data module 16 may include one or more modules or unitsto perform the various functions of present invention embodiments. As isdescribed in further detail below, the geographic data module 16 isgenerally configured to identify and visualize geographic data includedin a data set. In at least some embodiments, the client systems 14 maypresent a graphical user interface (e.g., GUI, etc.) or other such userinterface to allow a user to upload a set of data to the server systems10 so that geographic data can be identified and visualized.

Still referring to FIG. 1, the network environment 100 may also includea database system 18 that may store various information for thegeographic data module 16 (e.g., map data, map rendering instructions,etc.). The database system 18 may be implemented by any conventional orother system, such as a database or storage unit, may be local to orremote from server systems 10 and client systems 14, and may communicatevia any appropriate communication medium (e.g., local area network(LAN), wide area network (WAN), Internet, hardwire, wireless link,Intranet, etc.).

With reference now to FIG. 2, procedural flow chart 200 depictsoperations (e.g., of geographic data module 16) for identifying ordetecting geographic data and visualizing the identified geographicdata. Initially, at step 202, a set of data including candidategeographic data elements (also referred to herein as candidate elements,candidate data objects, etc.) is obtained. For example, a client systemmay send a data set to a server system upon which the geographic datamodule 16 is residing. In at least some embodiments, the data set is astructured data set, such that the input data has column names and a setof values. For example, a data set may include a table with a firstcolumn for a state and a second column for counties, such as thefollowing:

Alabama Hale Alabama Henry Alabama Houston Alabama Jackson AlabamaJefferson Alabama Lamar Alabama Laurderdale Alabama Lawrence Alabama LeeAlabama Limestone Alabama Lowndes Alabama Macon Alabama Madison AlabamaMarengo Alabama Marion Alabama Marshall

In this particular example, the candidate geographic data elements arethe strings included in the right column, but generally, the candidategeographic data elements may be any sub-elements within a data set. Thestrings or sub-elements are identified as candidate geographic dataelements using textual analysis, such as by utilizing pattern matchingto identify particular strings (e.g., lemmas and/or stems) andcross-referencing the identified strings with a set of known geographicterms (e.g., an established ontology or taxonomy of geographic terms).Additionally or alternatively, if the set of data includes labels, thelabels may be analyzed in a similar manner. Put another way, data in aset of data may be analyzed with a set of textual analysis rules toidentify candidate geographic data elements.

As an example, if the table above is analyzed with textual analysis,both columns may be determined to be candidate geographic data elements.In fact, a simple ontology might be able to easily determine that thedata set is providing a concept of counties within a state (Alabama);however, this table is merely an example and, in other embodiments, thedata set received may not indicate that the data is geographic data soclearly. For example, if another data set (e.g., a CSV file) onlyincludes the following data: “Hale, Henry, Houston, Jackson, Jefferson,Lawrence, Lee,” (without a state column or any metadata) it may beunclear if the data is geographic data or names. As another example, ifa data set includes the data element “Georgia” or the data element“Montgomery” without obvious context, it be unclear as to whether thedata element is referring to state, country, peach type, or name (e.g.,for Georgia), a city, county, name, street, etc. (e.g., for Montgomery),or some other entity. This becomes exponentially difficult as thegeographic data refers to lower hierarchies (e.g., counties, cities, oreven street names).

As is described in further detail below, the techniques presented hereinresolve these issues once a data set is obtained at step 202 byanalyzing a number of metrics associated with candidate data elements.The techniques provided herein can identify candidate geographic dataelements at any level of a geographic hierarchy (e.g., as low as streetaddresses and as high as continents) and consider any desirablegeographic field, ranging from postal codes, to regions, to provinces,to prefectures. In fact, in some embodiments, different hierarchies fromdifferent countries may be conflated in order to standardize geographicdata over the globe. For example, provinces in Canada may be equated tostates in the United States and regions in the United Kingdom (e.g.,England, Scotland, etc.).

At step 204, a best parent is determined for candidate geographic dataelements. The best parent is a data element that encompasses ordescribes the most sub-elements determined to be candidate geographicdata elements. In some embodiments, the best parent may be determined byidentifying an element with the best coverage of sub-elements within thedata. Additionally or alternatively, a best parent may be determined byidentifying or locating a parent element in the data. For example, inthe data set included in the table above, ‘Alabama’ may be the bestparent because it has a parent relationship with each piece of dataincluded in the right column and because it covers every sub-elementincluded in the right column. Alternatively, if the data set includes acolumn with values that are all clearly sub-elements of a particularparent, this parent may be identified by consulting a geographicresource or built in intelligence (even though the parent is notexplicitly included in the data set). For example, if the data set abovewas received without the first column, analysis may determine that allof the data elements in the second column are counties in Alabama and,thus, Alabama may be determined to be the best parent.

Moreover, in some embodiments, best parents may be found with recursivebehavior to increase the confidence level of a geographic datadetermination. For example, counties can be checked against states whichcan be checked against a country, and so forth. Since it is usuallyeasier to identify data as geographic data higher up in a geographichierarchy (e.g., states and countries typically have less overlap withcommonly used words or names), identifying a geographic best parenthigher in a hierarchy may provide increased confidence that thecandidate geographic data elements are indeed geographic data elements.For example, if an identified best parent state/province includes all ofthe candidate geographic data elements (e.g., county names); thecandidate geographic data elements may be likely to actually begeographic data elements.

At step 206, additional concepts associated with the candidategeographic data elements may be identified. Specifically, non-geographicconcepts associated with the candidate geographic data elements may beidentified so that other possible meanings of the candidate geographicdata elements may be considered. For example, if a data set includesJohn, Jefferson, and Georgia, a concept for people's names is identifiedas an additional concept that could describe the data. In at least someembodiments, the complete ontology of a data element is reviewed inorder to determine other concepts associated with the candidategeographic data elements. In fact, in at least some embodiments, theadditional concepts may not be considered unless the complete ontologyis available, since an incomplete ontology might improperly indicatethat no other concepts are associated with the candidate geographic dataelement.

At step 208, an average geographic distance between the candidategeographic data elements may be determined. The average may be a mean orweighted mean of the geographic distances between all of the candidategeographic data elements or a mean or weighted mean of the distancesfrom each candidate geographic data element to its closest candidategeographic data element. The distance between candidate geographic dataelements may be determined by measuring the distance between thelatitudinal and longitudinal coordinates of the centers of twogeographic areas. In some embodiments, this may be simplified byapproximating each geographic area as a shape and determining the centerin accordance with geometric formulas. Additionally or alternatively,the distances may be determined or adjusted in view of border locations(e.g., by analyzing GeoJSON border encodings). The borders may revealwhether two areas are bordering and, thus, may allow for a reduction ofa distance to zero when two geographic areas are bordering, even if thedistance between centers is measuring relatively high (which may be thecase in higher geographic hierarchies). Thus, large, but adjacentgeographic areas will not skew the results despite having two centersbeing separated by hundreds of miles (e.g., the centers of Colorado andWyoming).

At step 210, the candidate geographic data elements can be identified asgeographic data elements that should be displayed in a virtual map. Theidentification is based on at least one of the best parent, theadditional concepts, and the average distance (e.g., at least one of themetrics discussed above). In fact, in at least some embodiments, allthree metrics are considered in order to ensure accuracy. Each of themetrics determined in steps 204, 206, and 208 may be negatively orpositively correlated with the candidate geographic data elements beinggeographic and may be weighted or considered in any manner. In at leastsome embodiments, each of these metrics may be scored or numericallyrepresented and aggregated in order to determine a likelihood score, asis described in more detail below with regards to FIGS. 3 and 4. Then,when the likelihood score is above a certain threshold, candidategeographic data elements may be identified as geographic.

Generally, different metrics may be determined in connection with thebest parent and each of the different metrics may have a differentcorrelation, as is described in more detail in connection with FIGS. 3and 4. By comparison, the number of additional concepts associated withthe candidate geographic data elements is negatively correlated with thecandidate geographic data elements being geographic and, thus,additional identified concepts may decrease the chance of candidategeographic data elements being geographic (thereby decreasing thelikelihood that an accurate visualization should depict these elementson a map).

Similarly, the average geographic distance between candidate geographicdata elements may also be negatively correlated with the candidategeographic data elements being geographic so that a larger averagegeographic distance decreases the chance of candidate geographic dataelements being geographic. The average distance may have a negativecorrelation because when larger distances exist between elements, theelements are less likely to be useful when displayed on a map (e.g., therichness of a map may increase when focused on a smaller overall area).For example, if three counties within Texas are identified, thesubsequent map would only need to cover a portion of Texas, but if onecounty in USA, one county in Thailand, and one county in Brazil areidentified, the subsequently generated map would need to show nearly theentire globe and would not be able to provide much detail in any of theidentified locations. It is also less likely that a set of data includeswidely dispersed geographic data (and more likely that closely situatedgeographic elements are included in the same data set).

At step 212, candidate geographic data elements that have beenidentified as geographic are visualized. In particular, a map includingthe candidate elements is generated and displayed. In some embodiments,generating a map may involve generating display layers for the candidategeographic data elements and overlaying the generated display layersonto a base map. The map may focus on the particular region or regionsthat are covered by the candidate elements and, thus, may quicklyprovide a user with a useful view of geographic data. A generated mapmay also show any desirable details of a covered area, such as roadways,a satellite view, traffic patterns, etc. Moreover, since the techniquesprovided herein remove false positives, data that resembles geographicdata, but is not in fact geographic data will not be visualized in amapped environment, which might be confusing or unhelpful. In otherwords, the techniques provided herein may improve computer processingspeed and efficiency (e.g., in terms of time) by only processing thosedata sets that include geographic data elements for display on a map.

Now referring to FIG. 3, procedural flow chart 300 depicts operations(e.g., of geographic data module 16) for identifying geographic databased on the best parent. At step 302, an average hierarchal distance tothe parent may be determined. The hierarchal distance may measure orcount the number of levels between the candidate geographic dataelements and the best parent. For example, if a data set includes a fewcounties in the United States, one county (or equivalent geographicstructure) in Germany, and one county (or equivalent geographicstructure) in China, the common parent would be the Earth because thedata is spread over different countries and continents {North America,Europe, Asia} and the parent may separated from the candidate geographicdata elements by three levels (e.g., counties to states, to countries tocontinents to Earth). Similar to the average geographic distance betweencandidate geographic data elements, this distance negatively correlateswith the candidate geographic data elements being geographic. This is atleast because it may not be useful to present a user with a map that, bydefault, shows large expanses that are hierarchaly distant from thecandidate geographic data elements. As a specific example, in theexample above, every single county in the world would need to be mappedto show all of the candidate geographic data elements, even though onlyfive or six counties have data. In other words, this data would have arepresentation of 5/TOTAL_NUM_OF_COUNTIES_WORLD, which is essentially 0%and, thus, would be unlikely to be helpful if visualized in a map.

At step 304, coverage of the best parent can be determined. The coveragerelates to how many elements from the candidate geographic data elementsare associated with the best parent. As is described in further detailbelow, this metric may be positively correlated with candidategeographic data elements being geographic so that a best parent coveringmore candidate geographic data elements tends to show that the candidategeographic data elements are geographic and should be displayed in amap. For example, if a subset includes one or two counties from fivedifferent states in the USA, this may suggest that the data could beabout one to one with respective parents in the next highest hierarchy(e.g., states). By comparison, if the data set includes one or twostates with five counties each, each parent in the next highesthierarchy (e.g., states) may have wider coverage and this would providemore confidence that the data set is representing counties (as opposedto being a list of names that happens to correlate similarly to countynames in different states).

At step 306, a number of candidate geographic data elements included ata same level of geographic hierarchy under the best parent may bedetermined. In other words, if the best parent is a state that covers 15candidate geographic data elements, it is determined how many of thesecandidate geographic data elements are counties within the state, howmany are cities within the state, and so forth. As is described infurther detail below, this metric may be positively correlated withcandidate geographic data elements being geographic so that a bestparent with more candidates in the same hierarchy tends to show that thecandidates are geographic. This is at least because as the number ofcandidate geographic data elements in a specific hierarchy grows, theharder it becomes to find additional elements in the same hierarchy.Consequently, it is less likely that the candidate geographic dataelements are not geographic as more elements are found within the samehierarchy.

Now referring to FIG. 4, procedural flow chart 400 depicts operations(e.g., of geographic data module 16) for determining a likelihood score.The likelihood score represents the likelihood that the candidategeographic data elements are geographic. As mentioned above, the bestparent can impact the likelihood score in a number of manners.Consequently, at step 402, the likelihood score may be increased as theaverage distance to the best parent decreases (since this metric isnegatively correlated with the candidate geographic data elements beinggeographic), the number of candidate geographic data elements covered bythe best parent increases (since this metric is positively correlatedwith the candidate geographic data elements being geographic), or thenumber of candidate geographic data elements in a particular hierarchyincreases (since this metric is positively correlated with the candidategeographic data elements being geographic).

At step 404, the likelihood score may be increased as the number ofadditional concepts found by analyzing the complete ontology decreases.This is at least because the number of additional concepts found isnegatively correlated with the candidate geographic data elements beinggeographic. Finally, at step 406, the likelihood score may be increasedas the distance between the candidate geographic data elements decreasessince the distance between candidates is negatively correlated with thecandidate geographic data elements being geographic.

More specifically, in at least some embodiments, each of theaforementioned elements may be defined numerically by the followingequations:

The average coverage of the parent element may be represented as A anddefined as a real number (R) between 0 and 1, per {A|A ∈R and A ∈ [0,1]}, so that A defines the percentage of candidate elements covered bythe best parent.

The average distance to the best parent may be represented as B anddefined as a natural number (positive integer) between as 0 and 6, per{B|B ∈N and B ∈ [0, 6]}. In some embodiments, this range may representseven level of geographic hierarchy minus the existing level. However,if the depth of the geography considered is altered, this range may bealtered accordingly.

The count of recognizable geographic elements at the same level may berepresented as C and defined as a natural number between 1 and X, whereX represents the total number of rows included in the data set, per {C|C∈N and C ∈ [1, X]}, so that C is a count that cannot exceed the numberof rows.

The distance between the candidate geographic data elements may berepresented as D and may be a real number greater than 0, per {D|D ∈ Rand D>0 }. For example, D may be a distance between elements inkilometers.

The number of additional concepts associated with the candidategeographic data elements may be represented as E and may be a positiveinteger, per {E|E ∈ N }.

Moreover, in at least some embodiments, the number of rows may also beconsidered when determining the likelihood score since the number ofrows provides context for the data. As mentioned above, the number ofrows may be represented as X and may be any positive integer, per {X|D ∈N }.

Once a score is determined for each of the aforementioned metrics orelements (e.g., for A, B, C, D, E, and X), the score for each metric orelement may be weighted in an aggregation formula to determine alikelihood score, such as the following formula:

${likelihood}_{score} = \frac{A + \frac{C}{X}}{\left( {B + 1} \right)*D*E}$

Notably, the negatively correlated elements (B, D, and E) are includedon the bottom of the formula, while positively correlated elements (Aand C) are included at the top of the formula.

Once this aggregated likelihood score is determined, it may be compared,at step 408, to a threshold. If the score is above the threshold, thecandidate geographic data elements may be considered to be geographic.The threshold may be a predetermined threshold determined based ontesting or a dynamic threshold determined based on running averages andother such factors. However, the threshold should be high enough toaccurately determine when candidate data elements are geographic. Thatbeing said, in some embodiments, if one of the factors score very low,it may be possible to dump or ignore this factor (e.g., consider it anoutlier), especially if the other factors provide high confidence. Forexample, if a high number of additional concepts are identified, butevery other factor indicates that the data is geographic, the additionalconcepts may be ignored and the candidate geographic data elements maybe considered geographic if desired.

Referring generally to FIG. 4, in some embodiments, scores may bedetermined for any data objects or group of data objects determined tobe candidate geographic objects. Alternatively, the number of operationsmay be reduced by first determining scores for column names or labels(if they exist) and only subsequently analyzing candidate geographicdata elements when an associated column name or label is determined tobe geographic (e.g., when the likelihood score for a column or labelsatisfies the threshold). Additionally or alternatively, data elementsmay be filtered with any other method, such as imperfect ontologies thatsometimes produce false positives, before being analyzed with thetechniques presented herein (e.g., before a likelihood score iscalculated).

Referring now to FIG. 5, a schematic of an example of a computing nodeor device 510 for computer environment 100 (e.g., server systems 10 andclient systems 14, etc.) is shown. The computing node or device 510 isonly one example of a suitable computing node for computing environment100 and is not intended to suggest any limitation as to the scope of useor functionality of embodiments of the invention described herein.Regardless, computing node 510 is capable of being implemented and/orperforming any of the functionality set forth herein.

In computing node 510, there is a computer system 512 which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system 512 include, but are not limitedto, personal computer systems, server computer systems, thin clients,thick clients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 512 may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.Computer system 512 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

In FIG. 6, computer system 512 is shown in the form of a general-purposecomputing device. The components of computer system 512 may include, butare not limited to, one or more processors or processing units 516, asystem memory 528, and a bus 518 that couples various system componentsincluding system memory 528 to processor 516.

Bus 518 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system 512 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 512, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 528 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 530 and/or cachememory 532. Computer system 512 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 534 can be provided forreading from and writing to a nonremovable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 518 by one or more datamedia interfaces. As will be further depicted and described below,memory 528 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 540, having a set (at least one) of program modules 542,may be stored in memory 528 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 542 (e.g., including geographic data module16) generally carry out the functions and/or methodologies ofembodiments of the invention as described herein.

Computer system 512 may also communicate with one or more externaldevices 514 such as a keyboard, a pointing device, a display 524, etc.;one or more devices that enable a user to interact with computer system512; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 512 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces522. Still yet, computer system 512 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter520. As depicted, network adapter 520 communicates with the othercomponents of computer system 512 via bus 518. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 512. Examples, include, butare not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, RAID systems, tape drives, and dataarchival storage systems, etc.

The techniques provided herein have a number of advantages. As oneexample, the combination of factors considered to make a determinationas to whether a candidate geographic data element is geographic mayreduce false positives and provide confidence when mapping geographicdata. This may be beneficial for the plethora of applications that arenot utilizing location information, since false positives (e.g.,non-geographic data identified as geographic) are reduced. Moreover, fordata analysis and visualization, the techniques presented herein mayensure that data is visualized in the most useful manner. Moregenerally, the techniques provided herein resolve a problem that isrooted in computer technology by resolving computerized data analysisissues with recognizing geographic data. Without these techniques,non-geographic data may be identified as geographic which may causesubsequent issues with analysis, visualization, and other suchoperations. For example, family names might be incorrectly displayed ona map during data visualization.

It will be appreciated that the embodiments described above andillustrated in the drawings represent only a few of the many ways ofimplementing embodiments for identifying and virtualizing geographicdata.

The environment of the present invention embodiments may include anynumber of computer or other processing systems (e.g., client or end-usersystems, server systems, etc.) and databases or other repositoriesarranged in any desired fashion, where the present invention embodimentsmay be applied to any desired type of computing environment (e.g., cloudcomputing, client-server, network computing, mainframe, stand-alonesystems, etc.). The computer or other processing systems employed by thepresent invention embodiments may be implemented by any number of anypersonal or other type of computer or processing system (e.g., desktop,laptop, PDA, mobile devices, etc.), and may include any commerciallyavailable operating system and any combination of commercially availableand custom software (e.g., browser software, communications software,server software, geographic data module, etc.). These systems mayinclude any types of monitors and input devices (e.g., keyboard, mouse,voice recognition, etc.) to enter and/or view information.

It is to be understood that the software (e.g., geographic data module16) of the present invention embodiments may be implemented in anydesired computer language and could be developed by one of ordinaryskill in the computer arts based on the functional descriptionscontained in the specification and flow charts illustrated in thedrawings. Further, any references herein of software performing variousfunctions generally refer to computer systems or processors performingthose functions under software control. The computer systems of thepresent invention embodiments may alternatively be implemented by anytype of hardware and/or other processing circuitry.

The various functions of the computer or other processing systems may bedistributed in any manner among any number of software and/or hardwaremodules or units, processing or computer systems and/or circuitry, wherethe computer or processing systems may be disposed locally or remotelyof each other and communicate via any suitable communications medium(e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection,wireless, etc.). For example, the functions of the present inventionembodiments may be distributed in any manner among the variousend-user/client and server systems, and/or any other intermediaryprocessing devices. The software and/or algorithms described above andillustrated in the flow charts may be modified in any manner thataccomplishes the functions described herein. In addition, the functionsin the flow charts or description may be performed in any order thataccomplishes a desired operation.

The software of the present invention embodiments (e.g., geographic datamodule 16) may be available on a non-transitory computer useable medium(e.g., magnetic or optical mediums, magneto-optic mediums, floppydiskettes, CD-ROM, DVD, memory devices, etc.) of a stationary orportable program product apparatus or device for use with stand-alonesystems or systems connected by a network or other communicationsmedium.

The communication network may be implemented by any number of any typeof communications network (e.g., LAN, WAN, Internet, Intranet, VPN,etc.). The computer or other processing systems of the present inventionembodiments may include any conventional or other communications devicesto communicate over the network via any conventional or other protocols.The computer or other processing systems may utilize any type ofconnection (e.g., wired, wireless, etc.) for access to the network.Local communication media may be implemented by any suitablecommunication media (e.g., local area network (LAN), hardwire, wirelesslink, Intranet, etc.).

The system may employ any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., likelihood scores and formulas, map data, and other geographicinformation, etc.). The database system may be implemented by any numberof any conventional or other databases, data stores or storagestructures (e.g., files, databases, data structures, data or otherrepositories, etc.) to store information (e.g., likelihood scores andformulas, map data, and other geographic information, etc.). Thedatabase system may be included within or coupled to the server and/orclient systems. The database systems and/or storage structures may beremote from or local to the computer or other processing systems, andmay store any desired data (e.g., likelihood scores and formulas, mapdata, and other geographic information, etc.).

The present invention embodiments may employ any number of any type ofuser interface (e.g., Graphical User Interface (GUI), command-line,prompt, etc.) for obtaining or providing information (e.g., forsubmitting a data set or receiving a displayed map.), where theinterface may include any information arranged in any fashion. Theinterface may include any number of any types of input or actuationmechanisms (e.g., buttons, icons, fields, boxes, links, etc.) disposedat any locations to enter/display information and initiate desiredactions via any suitable input devices (e.g., mouse, keyboard,touchscreen, etc.). The interface screens may include any suitableactuators (e.g., links, tabs, etc.) to navigate between the screens inany fashion.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, “including”, “has”, “have”, “having”, “with”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

1-7. (canceled)
 8. An apparatus for identifying and visualizing geographic data comprising: at least one processor configured to: obtain a set of data including candidate geographic data elements; determine metrics based on two or more of: a best parent for the candidate geographic data elements; additional concepts associated with the candidate geographic data elements; and an average distance between the candidate geographic data elements; identify the candidate geographic data elements as geographic based on the metrics; and generate a map displaying the candidate geographic data elements identified as geographic.
 9. The apparatus of claim 8, wherein, in identifying, the at least one processor is configured to: generate a likelihood score based on an aggregation of a score associated with the best parent, a score associated with the additional concepts, and a score associated with the average distance; and identify the candidate geographic data elements as geographic when the likelihood score exceeds a threshold.
 10. The apparatus of claim 9, wherein, the at least one processor is further configured to: determine the score associated with the best parent by analyzing the candidate geographic data elements based on one or more from a group of: an average distance of the candidate geographic data elements to the best parent; a number of candidate geographic data elements covered by the best parent; and a number of candidate geographic data elements included at a same level of geographic hierarchy under the best parent, wherein there is a negative correlation between the likelihood score and the average distance, a positive correlation between the likelihood score and the number of candidate geographic data elements covered by the best parent, and a positive correlation between the likelihood score and the number of candidate geographic data elements included at the same level of geographic hierarchy under the best parent.
 11. The apparatus of claim 9, wherein the at least one processor is further configured to: determine the score associated with the additional concepts by analyzing a complete ontology of the candidate geographic data elements, wherein there is a negative correlation between the likelihood score and the number of additional concepts in the complete ontology.
 12. The apparatus of claim 9, wherein there is a negative correlation between the likelihood score and the average distance between the candidate geographic data elements.
 13. The apparatus of claim 9, wherein the aggregation further aggregates a score determined based on a number of rows included in the set of data
 14. The apparatus of claim 8, wherein data included in the set of data is structured data that does not include an indication as whether the data is geographic data.
 15. A computer program product for identifying and visualizing geographic data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by at least one processor to cause the at least one processor to: obtain a set of data including candidate geographic data elements; determine metrics based on two or more of: a best parent for the candidate geographic data elements; additional concepts associated with the candidate geographic data elements; and an average distance between the candidate geographic data elements; identify the candidate geographic data elements as geographic based on the metrics; and generate a map displaying the candidate geographic data elements identified as geographic.
 16. The computer program product of claim 15, wherein the program instructions to identify further comprise program instructions executable by the at least one processor to cause the at least one processor to: generate a likelihood score based on an aggregation of a score associated with the best parent, a score associated with the additional concepts, and a score associated with the average distance; and identify the candidate geographic data elements as geographic when the likelihood score exceeds a threshold.
 17. The computer program product of claim 16, further comprising program instructions executable by the at least one processor to cause the at least one processor to: determine the score associated with the best parent by analyzing the candidate geographic data elements based on one or more from a group of: an average distance of the candidate geographic data elements to the best parent; a number of candidate geographic data elements covered by the best parent; and a number of candidate geographic data elements included at a same level of geographic hierarchy under the best parent, wherein there is a negative correlation between the likelihood score and the average distance, a positive correlation between the likelihood score and the number of candidate geographic data elements covered by the best parent, and a positive correlation between the likelihood score and the number of candidate geographic data elements included at the same level of geographic hierarchy under the best parent.
 18. The computer program product of claim 16, further comprising program instructions executable by the at least one processor to cause the at least one processor to: determine the score associated with the additional concepts by analyzing a complete ontology of the candidate geographic data elements, wherein there is a negative correlation between the likelihood score and the number of additional concepts in the complete ontology.
 19. The computer program product of claim 16, wherein there is a negative correlation between the likelihood score and the average distance between the candidate geographic data elements.
 20. The computer program product of claim 15, wherein data included in the set of data is structured data that does not include an indication as whether the data is geographic data. 